Welcome to the second edition of ML News Monthly – Nov 2020!! Here are the key happenings this month in the Machine Learning field that I think are worth knowing about. It's important to note that the practice of Responsible AI encompasses more than just privacy and security; those aspects are important, of course, and are perhaps covered more in mainstream media, but Responsible AI also includes concerns around safety and reliability, fairness, and transparency and accountability. This post will examine the maturity of the Responsible AI space through the lens of several surveys and an ethnographic study. Google Research team talking about the most advanced findings on how to operationalize state-of-the-art models and how to make the best out of their runtime performance.

Speech recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. Speech recognition has many applications, such as home automation, mobile telephony, virtual assistance, hands-free computing, video games, and so on. This is the application of Speech recognition where the machine converts text into speech so that it could be easily listened. Ex: Speechify is a startup that focuses on creating Audiobooks from any text. Machine Translation (MT) is the task of automatically converting one natural language into another, preserving the meaning of the input text, and producing fluent text in the output language.

I was looking for a list of Machine Learning datasets for comparing Logistic Regression model but I couldn't find it easily. I spent some time curating it based on my need. This post is collection of such datasets which you can download for your use. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.

Natural Language Processing is the ability of a Computer program to understand and automatically manipulate the natural language like speech, text etc. According to Wikipedia, Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. The global NLP market size is expected to grow from $10.2 billion in 2019 to $26.4 billion in 2024. The major growth factors of the NLP market include the increase in smart device usage, growth in the adoption of cloud-based solutions and NLP-based applications to improve customer service, as well as the increase in technological investments in the healthcare industry. Legal research lies at the heart of the legal profession.

The main purpose of this post is to explore the different ways in which Logistic Regression can be applied to the dataset and hence understanding how the model actually works. The idea is not to solve the problem itself. This post doesn't focus on getting best score using different models however it assumes that there's only one model available for use. This is part of the series of posts to learn and share the details of Logistic Regression. If you're new to this kindly refer my earlier posts on the same topic: The data set has different features like Demographics, Behavioural which includes current smoker, cigarettes per day and Medical history and our task is to predict if the person has 10 year risk of coronary heart disease.

Voice tech is another application of Natural Language Processing and voice seems to be getting adopted quicker than any other major technology. Led by Amazon's Alexa, smart speakers' install base is expected to reach 200 million units worldwide by 2020. I have compiled a list of voice tech startups which are innovative in nature and are already in the market. This list is a starting point and probably far from exhaustive and is not sorted in any order. I will come up with another post with more startups added, in future. SoundHound develops voice-enabled AI and conversational intelligence technologies.

Jumper.ai is an AI-based platform that enables brands and SME's to instantly auto-reply and engage with customers. Its platform features conversational commerce, live chat, automated replies, bot builder, and abandoned cart recovery. Boxx.ai is a Bengaluru based artificial intelligence startup that helps e-commerce companies increase their conversion rates by displaying the most personalised products for each user. Boxx.ai predicts what each visitor is likely to buy next using its proprietary algorithms. This helps consumer internet companies curate a line of products and extend a highly personalised experience to each of its customers.

It focuses mostly on the Scikit-Learn library with an in-depth tour of some of the most useful methods in Machine Learning-- classifying, regression, a bit of clustering. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas MŸller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

In this blog post we'll try to understand how to do a simple classification on fruits data. Dataset contains fruit names as target variables and mass, width, height and color score as features. It is a simple data set with less than 100 training examples. To understand the distribution of fruit names let's plot count of each category using seaborn library. Looks like all the fruits are equally distributed except mandarin.

The purpose of any Machine Learning model is to build an equation corresponding to the data provided. For example, y mx c is an equation which predicts value of y when given with the value of x. Let's try to build a model which can predict the coefficients of the equation. We have taken output value as sum of 2xa, 3xb and 4xc. We'll train our model with the training dataset and we'll see if model is able to correctly come up with coefficients.